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This paper presents a rich knowledge representation language aimed atformalizing causal knowledge. This language is used for accurately and directlyformalizing common benchmark examples from the literature of actual causality.A definition of cause is presented and used to analyze the actual causes ofchanges with respect to sequences of actions representing those examples.

Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach